Do Advanced Language Models Eliminate the Need for Prompt Engineering in Software Engineering? (2411.02093v1)

Abstract: LLMs have significantly advanced software engineering (SE) tasks, with prompt engineering techniques enhancing their performance in code-related areas. However, the rapid development of foundational LLMs such as the non-reasoning model GPT-4o and the reasoning model o1 raises questions about the continued effectiveness of these prompt engineering techniques. This paper presents an extensive empirical study that reevaluates various prompt engineering techniques within the context of these advanced LLMs. Focusing on three representative SE tasks, i.e., code generation, code translation, and code summarization, we assess whether prompt engineering techniques still yield improvements with advanced models, the actual effectiveness of reasoning models compared to non-reasoning models, and whether the benefits of using these advanced models justify their increased costs. Our findings reveal that prompt engineering techniques developed for earlier LLMs may provide diminished benefits or even hinder performance when applied to advanced models. In reasoning LLMs, the ability of sophisticated built-in reasoning reduces the impact of complex prompts, sometimes making simple zero-shot prompting more effective. Furthermore, while reasoning models outperform non-reasoning models in tasks requiring complex reasoning, they offer minimal advantages in tasks that do not need reasoning and may incur unnecessary costs. Based on our study, we provide practical guidance for practitioners on selecting appropriate prompt engineering techniques and foundational LLMs, considering factors such as task requirements, operational costs, and environmental impact. Our work contributes to a deeper understanding of effectively harnessing advanced LLMs in SE tasks, informing future research and application development.

• The paper finds that traditional prompt engineering techniques are less beneficial with newer, advanced LLMs, especially reasoning models with inherent Chain-of-Thought capabilities.
• Reasoning LLMs outperform non-reasoning models on complex tasks requiring multiple steps but incur higher costs without significant performance gains on simpler tasks.
• The study advises selecting models and techniques based on task complexity, recommending efficient non-reasoning models for direct outputs and reasoning LLMs with minimal prompts for complex problems.

Overview of the Need for Prompt Engineering in Advanced LLMs for Software Engineering

The research paper titled "Do Advanced LLMs Eliminate the Need for Prompt Engineering in Software Engineering?" by Wang et al. considers the impact of evolving LLMs on the necessity and effectiveness of prompt engineering. The empirical paper reevaluates established prompt engineering techniques against newer, more advanced LLMs including non-reasoning models like GPT-4o and reasoning models such as o1-mini.

Summary of the Study

The research confronts three essential questions:

1. Whether traditional prompt engineering techniques still markedly enhance advanced LLMs.
2. The effectiveness of reasoning LLMs compared to non-reasoning models across specific tasks.
3. If the advantages of advanced LLMs justify their associated costs.

The paper applies these questions to three key software engineering tasks: code generation, code translation, and code summarization, utilizing established datasets such as HumanEval, CodeTrans, and CodeSearchNet. It assesses the influence of prompt engineering techniques including few-shot, CoT, critique, among others, across these tasks within the context of newer foundational models.

Key Findings

The findings suggest the following:

1. Effectiveness of Prompt Engineering: The paper finds that many traditional prompt engineering techniques are less beneficial with newer, more advanced LLMs. In particular, reasoning LLMs, with built-in CoT capabilities, show inherent advantages in complex reasoning tasks, making simpler prompting often more effective. Prompt engineering, when applied to non-reasoning models like GPT-4o, yields modest improvements, but these gains are considerably less than those reported for earlier models.
2. Performance of Reasoning LLMs: For tasks requiring multiple reasoning steps, reasoning LLMs outperform non-reasoning counterparts. However, in tasks not typically requiring deep reasoning, the performance difference diminishes. Additionally, reasoning models incur noticeable computational and time costs without equivalent enhancements in performance for simpler tasks, suggesting a nuanced balance between task complexity and model efficiency.
3. Practical Guidance on Model and Technique Selection: Given the cost and environmental impacts, the paper advises selecting models and techniques based on task complexity. When expecting short, direct outputs, non-reasoning models are recommended for their efficiency. In contrast, complex tasks that benefit from extended reasoning should leverage reasoning LLMs with minimal and well-structured prompts to maximize efficiency and output quality.

Implications and Future Directions

The paper's results signal a significant shift in how advanced LLMs might be used in software engineering. The increasing sophistication of reasoning models implies a gradual decline in the need for elaborate prompting strategies, especially for tasks involving intricate problem-solving and reasoning.

Future research could further refine the application of prompt engineering in LLMs by optimizing prompt strategies tailored specifically to reasoning capabilities. Moreover, exploring dynamic control over CoT length in reasoning models and aligning outputs more closely to task-specific requirements could reduce unnecessary computational overhead.

This research thus contributes to ongoing discourse on the adaptiveness of AI tools in changing technological environments, highlighting the importance of balancing performance improvements with operational costs and sustainability considerations in software engineering.